The techniques proposed in this thesis have shown the potential for improving understanding of how brain connectivity varies across populations. Nevertheless there are still a number of outstanding questions, for example how to reduce the impact of false positives on the connectivity analysis, how to implement the proposed tract anisotropy measure into the atlas-based approach, and how to correlate these results with the findings of functional imaging studies.
8.2.1 Reduction of false positives
The impact of false positives and the issue of thresholding has been a key concern of this thesis. In the presented work, the choice was taken not to threshold individual tracts. This is on account of the
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problems associated with choosing a consistent threshold across all tracts and all subjects. Instead, attempts were made to reduce the impact of false positives through use of tissue type information to constrain the tractography from passing across sulci. In addition, a population-based thresholding approach was introduced (section 6.4.2), which removed connections which appeared infrequently across the population. Nevertheless, the presence of false positives remains a limitation. In future this may be reduced by constraining the results of the tractography using a connectivity prior as proposed in Jbabdi et al [86], formed from histological or functional data. Alternatively, a population-based connectivity prior could be formed from thresholded tractography results obtained from the atlas, or tracts themselves might be constrained by a population based mask of tract projections [12].
8.2.2 Improving the atlas-based approach
At present the atlas-based approach is limited in two keys ways. First, the use of finite strain re- orientation limits the degree to which brains can be deformed during alignment. We believe improve- ments may be obtained by incorporating a preservation of principle directions approach. However, care will need to be taken to ensure that the re-orientation strategy does not change the representation of uncertainty at each voxel.
Second, since the projections of individual streamlines are not saved during tractography, it has not been possible to implement the ODF-based estimate of anisotropy, used in the native space approach. Instead, anisotropy was estimated from FA averaged over all voxels in the tract. In future, the ODF measure may be re-introduced by first fitting B-splines to the centre of each tract following the approach of Goodlett et al. [62]. Then ODFs may be fitted to these mean tracts using probabilistic interpolation.
8.2.3 Research into preterm brain development
In this thesis we have demonstrated that the proposed techniques are able to successfully detect differences in tissue microstructure by applying them to a phenomenon already well understood, ageing. However, a key test for these techniques will be how successfully they can be applied to clinical populations. In particular, we are interested in applying the techniques to preterm infant populations.
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Preterm birth has long been associated with cerebral white matter injury and neurological impairment. Our aim is to identify genetic markers and neurological phenotypes indicative of hindered development. However, application of these techniques to neonates will require careful adjustment of the protocol. In particular, registration between neonatal brain images is more complex due to rapidly changing brain structure and the frequent presence of motion artefacts. This may be improved by use of an additional rigid registration step prior to affine and non-rigid registration. Nevertheless, a key question remains over how to initialise the tractography. Adult brain templates are not suited to anatomical segmentation of neonatal brain images as brain size and morphology are very different. One option may be to implement a voxel-wise approach. However, this will vastly increase the dimension of the feature vectors for machine learning analysis, which may consequently reduce the stability of the PCA-MLDA approach. An alternative is to derive new segmentations. These could be obtained from functional data [65] or by connectivity based parcellation [23, 138].
8.2.4 Connectivity based parcellation of the cortex
The underlying connective matrix of the brain underpins brain function and influences cortical folding. Therefore, equivalent functional regions can be identified across subjects through comparison of their connectivity profiles. This has been demonstrated by connectivity-based parcellation of the thalamus [23], where parcellations have been shown to correspond with histology. In addition, Roca et. al, [138] perform cortical segmentation via clustering of whole-brain connectivity matrices, and derive clusters which reflect recognisable functional sub-units [138].
Connectivity based parcellation may allow clusters of voxels to be extracted as input as seeds for tractography. However, one limitation is that it cannot consistently reproduce clusters across subjects. This issue may be irrelevant if parcellation and subsequent tractography is performed in an atlas space. Alternatively, it may be possible to incorporate atlas-based approaches in a combined label- fusion, connectivity based parcellation approach (see Publications, [6]). This would use atlas-based segmentations to initialise a probabilistic tracking step, from which voxels could subsequently be re- classified on the basis of their connectivity profiles. This might allow adult atlases to be used as a starting point for neonatal brain segmentation.
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8.2.5 Drawing correspondences with functional imaging studies
Another key goal of neuroimaging is to better understand the structural correlates of functional con- nectivity. In Deligianni et al (see Publications, [7]) we used a predictive model based on PCA and canonical correlation analysis to infer functional connections from models of anatomical connectivity, where these were constructed using the framework proposed in Chapter 4. Similar collaborations are likely to play a central role in future research into the causes and outcomes of preterm birth.
8.3
Summary
This thesis has proposed a new direction for whole-brain connectivity analysis. It is clear that there are still a number of open questions and limitations to be overcome, before the technique can be reliably used to identify clinical biomarkers. Nevertheless, it is hoped that this technique can be used to improve understanding of how brain connections change during development and disease. The next step will be to implement the approach on a study of preterm brain development.
Appendix A
List of symbols and abbreviations
A.0.1 Abbreviations
Common abbreviations, in order of appearance:
Abbreviation Meaning
MRI Magnetic Resonance Imaging
GM Grey matter
WM White matter
CSF Cerebral Spinal Fluid
voxel volumetric pixel, 3D image element
ADC Apparent Diffusion Coefficient
FA Fractional Anistropy
DSI Diffusion Spectrum Imaging
ODF Orientation Distribution function
FFD Free Form Deformations
SSD Sum of Squared Differences
CC Cross correlation
NMI Normalised Mutual Information
ROI Region of Interest
SPM Statistical Parametric Mapping (software)
FSL FMRIBs software library (software)
PCA Principal component analysis
MLDA Maximum uncertainty Linear Discriminant Analysis
SVM Support Vector Machine
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